Using Privacy-Enhancing Technologies (PETs) for machine learning often influences the characteristics of a machine learning approach, e.g., the needed computational power, timing of the answers or how the data can be utilized. When designing a new service, the developer faces the problem that some decisions require a trade-off. For example, the use of a PET may cause a delay in the responses or adding noise to the data to improve the users' privacy might have a negative impact on the accuracy of the machine learning approach. As of now, there is no structured way how the users' perception of a machine learning based service can contribute to the selection of Privacy Preserving Machine Learning (PPML) methods. This is especially a challenge since one cannot assume that users have a deep technical understanding of these technologies. Therefore, they can only be asked about certain attributes that they can perceive when using the service and not directly which PPML they prefer. This study introduces a decision support framework with the aim of supporting the selection of PPML technologies based on user preferences. Based on prior work analysing User Acceptance Criteria (UAC), we translate these criteria into differentiating characteristics for various PPML techniques. As a final result, we achieve a technology ranking based on the User Acceptance Criteria while providing technology insights for the developers. We demonstrate its application using the use case of classifying privacy-relevant information. Our contribution consists of the decision support framework which consists of a process to connect PPML technologies with UAC, a process for evaluating the characteristics that separate PPML techniques, and a ranking method to evaluate the best PPML technique for the use case.

翻译：在机器学习中使用隐私增强技术（PETs）通常会改变机器学习方法的特性，例如所需的计算能力、响应时间或数据利用方式。在设计新服务时，开发者面临需要权衡取舍的决策问题。例如，采用PET可能导致响应延迟；为增强用户隐私而向数据添加噪声可能对机器学习方法的准确性产生负面影响。目前，尚缺乏结构化方法将用户对基于机器学习服务的感知纳入隐私保护机器学习方法的选择过程中。这一挑战尤为突出，因为不能假定用户对这些技术具备深入的技术理解。因此，只能询问用户在使用服务时可感知的特定属性，而非直接征询其对PPML技术的偏好。本研究提出一种决策支持框架，旨在基于用户偏好辅助PPML技术的选择。基于先前对用户接受标准的分析研究，我们将这些标准转化为区分不同PPML技术的特征指标。最终，我们实现了基于用户接受标准的技术排序，同时为开发者提供技术洞察。我们通过隐私相关信息分类的应用案例演示了该框架的实施。本研究的贡献在于构建了包含三个核心组件的决策支持框架：连接PPML技术与用户接受标准的流程、评估PPML技术区分特征的流程，以及针对具体用例评估最佳PPML技术的排序方法。